Online Processing of Social Media Data for Emergency Management

This source preferred by Hamid Bouchachia

Authors: Pohl, D., Bouchachia, A. and Hellwagner, H.

Start date: 4 December 2013

This data was imported from Scopus:

Authors: Pohl, D., Bouchachia, A. and Hellwagner, H.

Journal: Proceedings - 2013 12th International Conference on Machine Learning and Applications, ICMLA 2013

Volume: 1

Pages: 408-413

DOI: 10.1109/ICMLA.2013.83

Social media offers an opportunity for emergency management to identify issues that need immediate reaction. To support the effective use of social media, an analysis approach is needed to identify crisis-related hotspots. We consider in this investigation the analysis of social media (i.e., Twitter, Flickr and YouTube) to support emergency management by identifying sub-events. Sub-events are significant hotspots that are of importance for emergency management tasks. Aiming at sub-event detection, recognition and tracking, the data is processed online in real-time. We introduce an incremental feature selection mechanism to identify meaningful terms and use an online clustering algorithm to uncover sub-events on-the-fly. Initial experiments are based on tweets enriched with Flickr and YouTube data collected during Hurricane Sandy. They show the potential of the proposed approach to monitor sub-events for real-world emergency situations. © 2013 IEEE.

This data was imported from Web of Science (Lite):

Authors: Pohl, D., Bouchachia, A. and Hellwagner, H.

Journal: 2013 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2013), VOL 1

Pages: 408-413

DOI: 10.1109/ICMLA.2013.83

The data on this page was last updated at 04:40 on November 22, 2017.